---
title: Welcome to LlamaIndex.TS
description: LlamaIndex.TS is the leading framework for utilizing context engineering to build LLM applications in JavaScript and TypeScript.
---

LlamaIndex.TS is a **framework for utilizing context engineering to build generative AI applications** with large language models. From rapid-prototyping RAG chatbots to deploying multi-agent workflows in production, LlamaIndex gives you everything you need — all in idiomatic TypeScript.

Built for modern JavaScript runtimes like <SiNodedotjs className="inline" color="#5FA04E" /> **Node.js**, <SiDeno className="inline" color="#70FFAF" /> **Deno**, <SiBun className="inline" /> **Bun**, <SiCloudflareworkers className="inline" color="#F38020" /> **Cloudflare Workers**, and more.

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  <a href="#introduction" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
    <h3 className="mb-1 text-lg font-semibold underline">Introduction</h3>
    <p className="text-sm text-gray-400 no-underline">Context engineering, agents &amp; workflows — what do they mean?</p>
  </a>

  <a href="#use-cases" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
    <h3 className="mb-1 text-lg font-semibold underline">Use cases</h3>
    <p className="text-sm text-gray-400 no-underline">See what you can build with LlamaIndex.TS.</p>
  </a>

  <a href="#getting-started" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
    <h3 className="mb-1 text-lg font-semibold underline">Getting started</h3>
    <p className="text-sm text-gray-400 no-underline">Your first app in 5 lines of code.</p>
  </a>

  <a href="https://docs.cloud.llamaindex.ai/" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline" target="_blank" rel="noopener noreferrer">
    <h3 className="mb-1 text-lg font-semibold underline">LlamaCloud</h3>
    <p className="text-sm text-gray-400 no-underline">Managed parsing, extraction &amp; retrieval pipelines.</p>
  </a>

  <a href="#community" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
    <h3 className="mb-1 text-lg font-semibold underline">Community</h3>
    <p className="text-sm text-gray-400 no-underline">Join thousands of builders on Discord, Twitter, and more.</p>
  </a>

  <a href="#related-projects" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
    <h3 className="mb-1 text-lg font-semibold underline">Related projects</h3>
    <p className="text-sm text-gray-400 no-underline">Connectors, demos &amp; starter kits.</p>
  </a>
</div>

## Introduction

### What are agents?

[Agents](/typescript/framework/tutorials/agents/1_setup) are LLM-powered assistants that can reason, use external tools, and take actions to accomplish tasks such as research, data extraction, and automation. 
LlamaIndex.TS provides foundational building blocks for creating and orchestrating these agents.

### What are workflows?

[Workflows](/typescript/framework/tutorials/workflows) are multi-step, event-driven processes that combine agents, data connectors, and other tools to solve complex problems. 
With LlamaIndex.TS you can chain together retrieval, generation, and tool-calling steps and then deploy the entire pipeline as a microservice.

### What is context engineering?

LLMs come pre-trained on vast public corpora, but not on **your** private or domain-specific data. 
Context engineering bridges that gap by injecting the right pieces of your data into the LLM prompt at the right time. 
The most popular example is [Retrieval-Augmented Generation (RAG)](/typescript/framework/getting_started/concepts), but the same idea powers agent memory, evaluation, extraction, summarisation, and more.

LlamaIndex.TS gives you:

- **Data connectors** to ingest from APIs, files, SQL, and dozens more sources.
- **Indexes & retrievers** to store and retrieve your data for LLM consumption.
- **Agents and Engines** to query and use chat+reasoning interfaces over your data.
- **Workflows** for fine-grained orchestration of your data and LLM-powered agents.
- **Observability** integrations so you can iterate with confidence.

You can learn more about these concepts in our [concepts guide](/typescript/framework/getting_started/concepts).

## Use cases

Popular scenarios include:

- [LLM-Powered Agents](/typescript/framework/tutorials/agents/1_setup)
- [Indexing and Retrieval](/typescript/framework/tutorials/rag)
- [Extracting Structured Data](/typescript/framework/tutorials/structured_data_extraction)
- [Custom Orchestration with Workflows](/typescript/framework/tutorials/workflows)

## Getting started

The fastest way to get started is in StackBlitz below — no local setup required:

<iframe
  className="w-full h-[440px]"
  aria-label="LlamaIndex.TS Starter"
  aria-description="Interactive starter for LlamaIndex.TS"
  src="https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples?embed=1&file=starter.ts"
/>

Want to learn more? We have several tutorials to get you started:

- [Installation + Runtime Guide](/typescript/framework/getting_started/installation)
- [Create your first agent](/typescript/framework/tutorials/agents/1_setup)
- [Learn how to index data and chat with it](/typescript/framework/tutorials/rag)
- [Learn how to write your own workflows and agents](/typescript/framework/tutorials/workflows)

---

## LlamaCloud

Need an end-to-end managed pipeline? Check out **[LlamaCloud](https://cloud.llamaindex.ai/)**: best-in-class document parsing (LlamaParse), extraction (LlamaExtract), and indexing services with generous free tiers.

---

## Community

- [Twitter](https://twitter.com/llama_index)
- [Discord](https://discord.gg/dGcwcsnxhU)
- [LinkedIn](https://www.linkedin.com/company/llamaindex/)

We 💜 contributors! View our [contributing guide](https://github.com/run-llama/LlamaIndexTS/blob/main/CONTRIBUTING.md) to get started.

## Related projects

- [Python framework GitHub](https://github.com/run-llama/llama_index)
- [Python docs](https://docs.llamaindex.ai/)
- [create-llama](https://www.npmjs.com/package/create-llama) — scaffold a new project in seconds!
- [UI Components](https://ui.llamaindex.ai/) — build chat applications with our Next.js components.
